Traffic noise in the bedroom in association with being overweight or obese: a cross-sectional study of the Respiratory Health in Northern Europe cohort

doi:10.21203/rs.3.rs-2573652/v1

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Research Article

Traffic noise in the bedroom in association with being overweight or obese: a cross-sectional study of the Respiratory Health in Northern Europe cohort

https://doi.org/10.21203/rs.3.rs-2573652/v1

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published 27 Jun, 2023

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Background

Previous research suggests an association between road traffic noise and obesity, but current evidence is inconclusive. We aimed to investigate associations between self-reported traffic noise in bedrooms and self-measured obesity markers.

Methods

We applied data from the Respiratory Health in Northern Europe (RHINE) cohort. We used self-measured waist circumference (WC) and body mass index (BMI) as outcome values. Noise exposure was assessed as perceived traffic noise in the bedroom and/or the bedroom window’s location towards the street. We applied linear, and logistic regression models and evaluated effect modifications by sex.

Results

Women, who reported very high traffic noise levels in bedroom, had 1.35 (95% CI 0.32–2.38) kg/m² higher BMI and 4.66 (95% CI 1.83–7.48) cm higher WC compared to women, who reported no traffic noise in the bedroom. Women who reported higher exposure to road traffic noise had statistically significant higher odds of being overweight, obese or have abdominal obesity with OR varying from 1.16 to 1.67 compared to women, who reported no traffic noise in the bedroom. Among men we did not find any clear associations between noise pollution and obesity measures.

Conclusion

Our results suggest that self-reported traffic noise in the bedroom may be associated to being overweight or obese among women, but not among men.

Noise

Obesity

Overweight

Adiposity

Indoor

Nocturnal

Self-reported

Road Traffic

Environmental noise is one of the most important environmental stressors impacting health (Hänninen et al., 2014; WHO, 2011a). The main source of environmental noise in Europe is road traffic, which is a growing environmental concern due to urbanization and increasing traffic volumes in cities world-wide (EEA, 2020; WHO, 2018). Exposure to traffic noise has been linked to quality of life and wellbeing (Clark et al., 2018), mental health (Dzhambov and Lercher, 2019), sleep disturbance (Basner et al., 2018) as well as cardiovascular and metabolic health, including being overweight or obese (Gui et al., 2022; Münzel et al., 2021; van Kempen et al., 2018, 2017).

Recently, adiposity has received great attention in public health as cases are rapidly increasing worldwide (WHO, 2011b). Obesity is one of the main risk factors contributing to the global burden of disease (Stanaway et al., 2018). Most of the deaths related to obesity, measured as increased waist circumference (WC) or elevated body mass index (BMI), result as cardiovascular disease; however, several studies suggest that obesity is a risk factor also for diabetes, cancer, and for many other non-communicable diseases (Afshin et al., 2017; WHO, 2011b). Well-recognized risk factors for obesity are behavioral risk factors, like physical inactivity and a high-calorie diet (Pengpid and Peltzer, 2017), but so far, little is known about the role of environmental risk factors.

Besner et al. (2014) have posited that road traffic noise may contribute to increased risk of obesity/being overweight through stress reactions. Traffic noise can induce acute stress with release of stress hormones depending on an individual’s reaction. If they respond through the active strategy “fight or flight”, the response involves activation of the sympathetic–adrenal–medullar axis and catecholamine release. The response through the repressed strategy “defeat” associated with distress, anxiety, etc. involves hypothalamic–pituitary–adrenocortical (HPA) activation and cortisol release(Daiber et al., 2019; Recio et al., 2016). HPA activity dysregulation has been reported to contribute to obesity and other metabolic complications(Bose et al., 2009). Moreover, stress reactions can be mediated by sleep disturbance(Zaharna and Guilleminault, 2010). During sleep, the brain is still able to perceive, evaluate and react to environmental sounds, and the noise levels that cause stress reactions are much lower during sleep. A stress response during sleep has been observed from very low noise levels as L_Amax 33 dB (Basner et al., 2014). Traffic noise in the bedroom can also lead to sleep deprivation (Zaharna and Guilleminault, 2010). Sleep deprivation affects appetite and energy expenditure regulating hormones(Lin et al., 2020). Numerous epidemiological studies link sleep deprivation with obesity (Koren and Taveras, 2018).

Exposure to traffic noise has been increasingly reported to be associated with adiposity, but the evidence is still limited and the results are rather controversial (An et al., 2018; Gui et al., 2022; Oftedal et al., 2015; Pyko et al., 2017; Wallas et al., 2019). In all studies, included in the systematic review conducted by van Kempen et al. (2018), increased traffic noise was associated with increases in obesity markers, but the evidence was evaluated to be “low”, because of the limited number of studies available in this topic. Another systematic review suggested that noise from different sources (transportation, leisure, occupational) tends to be positively associated with waist circumference, but not with BMI (An et al., 2018). Wallas et al. (2019), found an association between road traffic noise and BMI among school-age children. A Swedish cohort study found an association between residential levels of road traffic noise and WC (Pyko et al., 2017). Moreover, some groups of people seem to be more vulnerable, as in a Norwegian cross-sectional study, no associations with WC or BMI were found in the total population, but associations were statistically significant among highly noise-sensitive women (Oftedal et al., 2015).

In most of the epidemiological studies, the modelled noise levels at the façade have been applied; however, individual actual noise exposure depends on numerous factors, including the floor of the dwelling, façade insulation, bedroom location, hours spent at home, etc. (Wu et al., 2019). Many traffic noise models have been developed not considering all factors like different seasons, different days of week, different conditions (like dry and wet surface) of road etc. (Mann and Singh, 2022). Therefore, studies that use outdoor modelled noise levels as the exposure variable, can under- or overestimate the individual noise exposure. Indoor nocturnal noise measured with a noise dosimeter in participants' bedrooms during a week was associated with BMI in women but not in men in Taiwan (Li et al., 2021). Huang et al. (Huang et al., 2020) combined self-reported noise perception with data from noise-monitoring stations and found road traffic noise to be associated with all of the metabolic syndrome’s components, including abdominal obesity in a cohort study in Taiwan. Nevertheless, evidence is very limited about indoor nocturnal road traffic noise effects on obesity and being overweight.

The aim of the present study was to assess the association between self-reported nocturnal noise exposure and self-measured markers of overweight/obesity.

Study population

We applied data collected through self-administrated questionnaires and self-measurements from the cohort study Respiratory Health in Northern Europe (RHINE) III; although, this analysis has a cross-sectional design. The RHINE was initiated in 1989–1992, with 3000–4000 randomly selected men and women 17–45 years of age from each of the seven study centers: Reykjavik, Iceland; Bergen, Norway; Umeå, Uppsala and Gothenburg, Sweden; Aarhus, Denmark and Tartu, Estonia. RHINE III (after 20 years follow-up) was conducted in 2010–2012 and a total of 13,578 people in age 38–66 years (mean 51.6, SD 7.2) responded to the questionnaire (Johannessen et al., 2014). Sixty-one percent of the original respondents participated in RHINE III (Johannessen et al., 2014). The participants received a measuring tape and completed a questionnaire covering their health, lifestyle, indoor and outdoor environment, family history, socio-economic factors, sleep disturbance, noise hearing in participants’ bedrooms, and the bedrooms' location toward the street.

We included all participants for whom the necessary data were available for the analysis (in total 13,146). We excluded people who reported unrealistic waist circumferences, height, and weight data. People who reported that they spend less than 5 hours at home per day were also left out as this would cause exposure misclassification.

Noise Exposure Assessment

Participants were asked to evaluate noise exposure in their bedroom through the two questions:

In your bedroom, can you hear traffic noise?

Answer alternatives: (1) not at all, (2) a little, (3) a great deal, and (4) very much

Does your bedroom window face a nearby street (< 20 m)?

Answer alternatives: (1) no, (2) yes, a street with little traffic, (3) yes, a street with moderate traffic, and (4) yes, a street with a great deal of traffic

Different categorizations of noise exposure were used. For question 1, about traffic noise hearing in the bedroom, four categories (not at all, a little, a great deal, very much) and a binary variable (no /yes) were used. In the binary variable, participants who answered “not at all” were categorized as “no” and those who answered “a little, a great deal or very much” were categorized as “yes.” For question 2: “Does your bedroom window face a nearby street (< 20 m)?” four categories (no, a street with little traffic, a street with moderate traffic, a street with a great deal of traffic) and a binary variable (no/yes) were used. In addition, we composed a combined indicator. In the combined indicator, we considered being exposed only those who reported ‘yes’ to both – bedroom window location toward a street and traffic noise hearing in the bedroom. Not exposed were considered participants who had either ‘no’ window towards the street and/or ‘no’ traffic noise in bedroom.

Assessment Of Outcome

The assessment of outcomes was based on self-reported data in the RHINE III questionnaire. As a marker of abdominal obesity, we used WC. A measuring tape was sent to the participants together with the questionnaire, and they were asked to measure their waist at the level of the navel, while standing and under the clothes. We applied World Health Organization (WHO) suggested cut-off value to define abdominal obesity: ≥102 cm for men and ≥ 88 cm for women (WHO, 2011b). As a marker of general obesity, we used BMI, calculated as weight (kg) divided by height (m²) with a cutoff at BMI ≥ 25 to define being overweight and BMI ≥ 30 to define obesity.

Confounders

Potential confounders were chosen based on literature and using the software DAGitty (www.dagitty.net). To estimate the minimal adjustment set, and better visualize the different possible confounders, we developed a Directed Acyclic Graph (DAG) (Fig. S1). Crude models were adjusted for age and sex.

According to software DAGitty, minimal sufficient adjustment sets for estimating the total effect of noise hearing in the bedroom on obesity/being overweight were: 1) hours spent at home; study center, type of accommodation; or 2) study center; type of accommodation; age; working status.

The fully adjusted model included: sex, age (continuous), study center, smoking status (current, former, never), marital status (single, married or cohabitating, divorced or widowed), working status (yes, no), education (primary school, secondary or technical school, university or college), dwelling type (detached house, semidetached or terraced house, apartment), hours spent at home (continuous), exercise level (less than once a week, once a week, 2–3 times a week, daily) and having children (yes, no).

We also investigated how the associations are affected by air pollution, study centers, parent obesity history, and residential history. As the air pollution indicator, we used annual average concentrations of nitrogen oxides (NO_X) for the years 2009–2011 modelled for the participants’ home addresses from a previous study of the same cohort (Carlsen et al., 2017). When we adjusted models for NOx, we used continuous NO_X values as log values, because of the positively skewed distribution of NO_X. In stratified analysis we used NO_X in tertiles: low (≤ 8.18 µg/m³), medium (8.19–10.69 µg/m³) and high (≥ 10.7 µg/m³). We had air pollution data for 9655 participants. Family history of obesity was evaluated by the question “What picture best describes the body shape of each of your biological parents at age 50 years?” Participants could choose between body shape pictures numbered 1 to 9. Body silhouettes as a tool to reflect obesity in the past are described by (Lønnebotn et al., 2018). When one or both biological parents' body silhouettes were marked 5 or upwards, the participant was considered to have a family history of obesity (Lønnebotn et al., 2018). Participants were divided into two groups by residential history: 1) the same address for 10 years, and 2) the address had changed over 10 years.

Statistical Analyses

Differences in descriptive statistics were assessed for continuous variables, with a T-test or Mann-Whitney U-test, and for categorical variables a Pearson’s χ² test. Bonferroni correction was used for multiple comparisons. To analyze the relationship between noise exposure and the continuous outcomes WC and BMI, linear regression models were used. Binary outcomes (being overweight, obesity, and central obesity) were analyzed using logistic regression models. The categorical noise exposure was included both as indicator variables providing separate estimates for each category, and as a continuous variable with categories numbered 1, 2, 3, and 4 to estimate slope coefficients.

We first ran the crude models with adjustment for age and sex. Then, we ran multiple regression models, with adjustment for all confounders. We added interaction terms to the models to estimate possible effect modification with sex, physical activity, air pollution group, study center, residential history, and family history of obesity. A p-value of less than 0.05 was considered statistically significant. Because of statistically significant effect modification with sex, the analyses were stratified by sex.

The assumptions of the linear regression models were tested using normal probability plots and scatter plots of residuals against the predicted values (homoscedasticity). The relationships between continuous variables and obesity markers were checked for linearity. Multicollinearity was assessed using the variance inflation factor (VIF). A VIF > 5 was considered an indication of the presence of multicollinearity. The Durbin Watson statistic was used to test for autocorrelation. Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 26.0 (Armonk, NY IBM Corp).

In total, 13,096 participants answered the question “In your bedroom, can you hear traffic noise?” and 13,088 participants answered the question “Does your bedroom window face a nearby street (< 20 m)?”. Half of the participants (51.5%) reported that they can hear traffic noise in their bedroom. The highest proportion of participants who reported that they can hear traffic noise in their bedroom was in Tartu (72.9%) and lowest was in Gothenburg (38.9%). Altogether 42.6% of the participants reported that their bedroom window faces a nearby street closer than 20 m, with the highest proportion in Reykjavik (63.6%) and the lowest in Aarhus (34.1%).

The mean BMI was 26.0 (SD 4.5) kg/m², and it varied between different study centers. The highest mean BMI was in Reykjavik (27.2, SD 4.6) kg/m² and the lowest in Aarhus (25.2, SD 4.6) kg/m² (Fig. S2). The mean waist circumference was 87.2 (SD 12.3) cm among women and 97.5 (SD 11.1) cm among men (Fig. S3 and S4). WC was significantly different by study centers – being the highest among women in Gothenburg (89.9, SD 12.2 cm) and lowest in Aarhus (83.9, SD 11.9 cm). The highest WCs among men were in Reykjavik (100.3, SD 12.3 cm) and in Tartu (100.2, SD 13.2 cm) and the lowest in Aarhus (93.6, SD 9.8 cm). The prevalence of being overweight was 54.9% (lowest in Aarhus 45.7% and highest in Reykjavik 66.3%), and the prevalence of obesity was 15.3% in the study population. The prevalence of abdominal obesity was 44.6% among women and 31.1% among men, and it varied from 31.9% in Aarhus to 54.0% in Gothenburg for women and from 16.9% in Aarhus to 42.1% in Reykjavik for men.

There were some statistically significant differences in the study population characteristics according to noise exposure (Table 1). People who reported higher noise levels in their bedroom and/or had their bedroom window facing the street were more likely women, current smokers, less educated, unemployed, single or divorced, without children, with lower physical activity, lived in apartments, and spent more time at home.

Table 1

Descriptive characteristics of the study population in relation to self-reported traffic noise in bedroom. The number of participants in each group with percentages in brackets and mean values with+- standard deviation are reported
	In your bedroom, can you hear traffic noise?		Does your bedroom window face a nearby street (< 20 m)?
	No	Yes	No	Yes
Age (years)	51.7 (± 7.3)	51.5 (± 7.2)	51.6 (± 7.2)	51.6 (± 7.2)
Sex
Women	3255 (51.3)*	3680 (54.5)*	3900 (52.0)*	3029 (54.3)*
Men	3091 (48.7)*	3070 (45.5)*	3607 (48.0)*	2552 (45.7)*
Smoking status
Current	739 (11.8)*	912 (13.9)*	893 (12.2)*	758 (13.9)*
Former	2436 (39.0)	2638 (40.3)	2878 (39.3)	2194 (40.2)
Never	3073 (49.2)*	2989 (45.7)*	3551 (48.5)*	2504 (45.9)*
Education
Primary school	704 (11.1)	775 (11.6)	793 (10.6)	681 (12.3)*
Secondary or technical school	2566 (40.6)*	2867 (42.7)*	3134 (42.0)	2298 (41.4)
University or college	3044 (48.2)*	3067 (45.7)*	3540 (47.4)	2568 (46.3)
Working status
No	785 (12.5)*	1043 (15.6)*	1013 (13.6)	811 (14.6)
Yes	5519 (87.5)*	5650 (84.4)*	6438 (86.4)	4727(85.4)
Type of accomodation
Detached house	3520 (56.7)*	3093 (46.6)*	4128 (56.2)*	2487 (45.3)*
Semidetached or terraced house	1065 (17.1)*	1047 (15.8)*	1224 (16.7)	887 (16.2)
Apartment	1627 (26.2)*	2498 (37.6)*	1999 (27.2)*	2118 (38.6)*
Marital status
Single	537 (8.5)*	817 (12.3)*	708 (9.5)*	644 (11.7)*
Married or cohabitating	5173 (82.4)*	5034 (75.6)*	5994 (80.6)*	4206 (76.3)*
Divorced or widowed	571 (9.1)*	808 (12.1)*	717 (9.7)*	662 (12.0)*
Parent obesity
No	1310 (22.9)	1365 (22.6)	1563 (23.1)	1110 (22.3)
Yes	4416 (77.1)	4669 (77.4)	5216 (76.9)	3864 (77.7)
Children
No children	745 (11.8)*	1002(14.9)*	963 (12.9)	782 (14.1)
One or more children	5576 (88.2)*	5718 (85.1)*	6513 (87.1)	4776 (85.9)
Level of exercise
Less than once a week	1406 (22.3)	1495 (22.5)	1606 (21.6)*	1289 (23.4)*
Once a week	1156 (18.4)	1197 (18.0)	1335 (18.0)	1017 (18.5)
2–3 times a week	2405 (38.2)	2458 (37.0)	2813 (37.9)	2048 (37.2)
Daily	1325 (21.1)*	1495 (22.5)*	1669 (22.5)*	1151 (20.9)*
Mean hours spent at home	13.7 (± 3.2)*	13.9 (± 3.4)*	13.7 (± 3.3)*	13.9 (± 3.2)*
Waist circumference men	97.6 (± 11.0)	97.3 (± 11.2)	97.2 (± 11.0)*	97.8 (± 11.2)*
Waist circumference women	86.9 (± 11.9)*	87.5 (± 12.7)*	86.8 (± 12.1)*	87.8 (± 12.5)*
BMI	25.9 (± 4.3)*	26.1 (± 4.7)*	25.9 (± 4.4)*	26.2 (± 4.6)*
Obesity
No	5414 (85.7)*	5636 (83.7)*	6399 (85.5)*	4647 (83.6)*
Yes	906 (14.3)*	1095 (16.3)*	1083 (14.5)*	914 (16.4)*
Overweight
No	2868 (45.7)	2969 (44.5)	3423 (46.2)*	2411 (43.7)*
Yes	3403 (54.3)	3696 (55.5)	3984 (53.8)*	3110 (56.3)*
Abdominal obesity men (WC > 102)
No	1937 (67.9)	1990 (70.0)	2319 (69.3)	1609 (68.5)
Yes	916 (32.1)	858 (30.0)	1026 (30.7)	714 (31.5)
Abdominal obesity women (WC > 88)
No	1697 (56.6)*	1859 (54.1)*	2075 (57.1)*	1478 (52.9)*
Yes	1300 (43.4)*	1578 (45.9)*	1560 (43.3)*	1315 (47.1)*

*The differences between exposed and not exposed are statistically significant (p < 0.05).

We found statistically significant positive associations in crude and main models between self-reported traffic noise in the bedroom and the BMI in the total population (Table 2). Associations between self-reported traffic noise in the bedroom and WC appeared only in crude models in the total population.

Table 2

Associations between self-reported noise in bedroom and continuous obesity markers in total population
	Model	BMI, kg/m²		WC, cm
		N	Slope (= beta) (95% CI)	N	Slope (= beta) (95% CI)
In your bedroom, can you hear traffic noise? (no, little, much, very much)	Crude	13,074	0.21 (0.09 to 0.32)*	12,113	0.37 (0.05 to 0.69)*
	Main	11,595	0.15 (0.03 to 0.27)*	10,798	0.01 (-0.31 to 0.34)
Does your bedroom window face a nearby street (< 20 m)? (no, street with little traffic, street with moderate traffic, street with much traffic)	Crude	13,066	0.20 (0.10 to 0.30)*	12,107	0.53 (0.26 to 0.81)*
	Main	11,588	0.13 (0.02 to 0.23)*	10,793	0.20 (-0.09 to 0.49)
Participant reported at the same time bedroom window towards street and traffic noise hearing (yes/no)	Crude	13,052	0.30 (0.14 to 0.45)*	12,069	0.53 (0.08 to 0.98)*
	Main	11,580	0.20 (0.04 to 0.37)*	10,786	0.16 (-0.30 to 0.62)

*p < 0.05. Crude model is adjusted for sex and age (continuous) only. The main model is adjusted for sex, age (continuous), study center, smoking status, marital status, working status, education, dwelling type, hours spent at home (continuous), number of children (yes/no) and exercise level.

We found a significant effect modification by sex. The positive associations were statistically significant mainly for women. Women who reported that they could hear very much traffic noise in their bedroom had on average 1.35 (95% CI 0.32 to 2.38) kg/m² higher BMI and 4.66 (95% CI 1.83 to 7.48) cm higher WC compared to women who could not hear the traffic noise in the bedroom (Table 3). For men no associations and an opposite association were found. Men who could hear much traffic noise in their bedroom had smaller WC. We also identified an exposure-response association – the higher noise hearing corresponded to higher beta values for women and lower values for men (Table 3).

Women who reported their bedroom window location nearby a street with much traffic had a 2.22 (95% CI 0.33 to 4.10) cm higher WC compared to women who reported no bedroom window nearby street, but no statistically significant associations were found with BMI. More traffic in the street nearby bedroom window corresponded to higher beta values for women (Table 3). For men, there was no associations between bedroom window location and obesity markers.

Women who had a bedroom window nearby the street and reported at the same time noise hearing in bedroom had 0.31 (95% CI 0.07 to 0.56) kg/m² higher BMI and 0.67 (95% CI 0.01 to 1.32) cm higher WC. No associations were found for men.

Table 3

Associations between self-reported traffic noise in bedroom and continuous obesity markers stratified by sex
		BMI, kg/m²		WC, cm
		N	Slope (= beta) (95% CI)	N	Slope (= beta) (95% CI)
In your bedroom, can you hear traffic noise?	Women(ref no)	2902		2687
	Little	2759	0.36 (0.13 to 0.60)*	2597	0.87 (0.23 to 1.52) *
	Much	349	0.37 (-0.14 to 0.88)	323	1.15 (-0.22 to 2.52)
	Very much	75	1.35 (0.32 to 2.38)*	68	4.66 (1.83 to 7.48)*
	Men(ref no)	2776		2566
	Little	2405	-0.00 (-0.21 to 0.23)	2242	-0.21 (-0.80 to 0.38)
	Much	264	-0.34 (-0.80 to 0.13)	242	-1.72 (-3.09 to -0.35)*
	Very much	45	-0.56 (-1.64 to 0.52)	43	-1.60 (-4.70 to 1.50)
Does your bedroom window face a nearby street (< 20 m)?	Women(ref no)	3398		3189
	Toward street with little traffic	2027	0.08 (-0.17 to 0.33)	1883	0.01 (-0.66 to 0.68)
	Moderate traffic	470	0.35 (-0.08 to 0.80)	442	0.87 (-0.31 to 2.04)
	Much traffic	175	0.63 (-0.06 to 1.38)	158	2.22 (0.33 to 4.10)*
	Men(ref no)	3203		2976
	Toward street with little traffic	1759	0.11 (-0.11 to 0.33)	1641	0.35 (-0.28 to 0.98)
	Moderate traffic	403	-0.02 (-0.40 to 0.36)	361	-0.66 (-1.80 to 0.47)
	Much traffic	122	-0.46 (-1.12 to 0.20)	113	-1.64 (-3.57 to 0.30)
Participant reported at the same time bedroom window towards street and traffic noise hearing (yes/no)	Women(ref no)	4150		3871
	Yes	1915	0.31 (0.07 to 0.56)*	1796	0.67 (0.01 to 1.32)*
	Men(ref no)	3909		3627
	Yes	1575	0.00 (-0.21 to 0.22)	1462	-0.24 (-0.86 to 0.39)

*p < 0.05. Model adjusted for: age (continuous), study center, smoking status, marital status, working status, education, dwelling type, hours spent at home (continuous), exercise level and having children (yes/no).

Similar associations for continuous obesity outcomes appeared also for binary outcomes. In the total population higher levels of noise reported in the bedroom were associated with higher risks for being overweight (OR 1.10; 95% CI 1.02 to 1.20) and obesity (OR 1.13; 95% CI 1.01 to 1.26). Being overweight was associated also with bedroom location nearby a street. Results for the total population (crude and main models) are shown in supplementary material (Table S1).

In stratified analysis, it appeared that women who reported higher exposure to road traffic noise in the bedroom had an increased risk of being overweight, obesity, and abdominal obesity with OR varying from 1.13 to 2.06 in crude models and from 1.16 to 1.67 in fully adjusted models (Table 4). Risks increased with increasing exposure levels, although the differences were not always statistically significant probably due to a lower sample size in higher exposure levels. For men, the associations were again rather opposite. Men, who reported much or very much traffic noise in the bedroom, had a statistically significantly lower risk of abdominal obesity (Table 4).

Table 4

Associations between self-reported traffic noise in bedroom and binary obesity markers stratified by sex
		Overweight^a	Obesity^b	Abdominal obesity^c
OR (95% CI)
In your bedroom, can you hear traffic noise?	Women(ref no)
	Little	1.19 (1.07 to 1.33)*	1.20 (1.02 to 1.40)*	1.21 (1.08 to 1.36)*
	Much	1.16 (0.92 to 1.47)	1.24 (0.91 to 1.69)	1.18 (0.92 to 1.51)
	Very much	1.55 (0.95 to 2.55)	1.56 (0.90 to 2.71)	1.67 (1.00 to 2.28)*
	Men(ref no)
	Little	1.01 (0.89 to 1.14)	1.06 (0.90 to 1.24)	0.92 (0.01 to 1.05)
	Much	0.87 (0.66 to 1.15)	0.87 (0.61 to 1.25)	0.73 (0.53 to 0.99)*
	Very much	0.92 (0.49 to 1.74)	0.85 (0.37 to 1.99)	0.41 (0.18 to 0.91)*
Does your bedroom window face a nearby street (< 20 m)?	Women(ref no)
	Toward street with little traffic	1.06 (0.94 to 1.19)	0.98 (0.83 to 1.15)	1.06 (0.94 to 1.20)
	Moderate traffic	1.24 (1.02 to 1.52)*	1.05 (0.79 to 1.38)	1.19 (0.96 to 1.147)
	Much traffic	1.02 (0.74 to 1.40)	1.50 (1.01 to 2.19)*	1.32 (0.94 to 1.85)
	Men(ref no)
	Toward street with little traffic	0.96 (0.85 to 1.09)	1.09 (0.92 to 1.29)	1.00 (0.87 to 1.15)
	Moderate traffic	1.16 (0.92 to 1.46)	0.75 (0.54 to 1.04)	0.82 (0.63 to 1.06)
	Much traffic	0.85 (0.58 to 1.24)	0.58 (0.31 to 1.08)	0.72 (0.56 to 1.14)
Participant reported at the same time bedroom window towards street and traffic noise hearing (yes/no)	Women(ref no)	1.18 (1.05 to 1.32)*	1.12 (0.96 to 1.31)	1.16 (1.03 to 1.30)*
	Men(ref no)	0.98 (0.86 to 1.12)	0.98 (0.83 to 1.16)	0.91 (0.79 to 1.05)

^a BMI ≥ 25 kg/m², ^b BMI ≥ 30 kg/m², ^c women WC > 88 cm; men WC > 102 cm, *p < 0.05. Model is adjusted for: age (continuous), study center, smoking status, marital status, working status, education, dwelling type, hours spent at home (continuous) and exercise level.

We also investigated how the associations between noise hearing in the bedroom and BMI are affected by other factors such as air pollution, study center, family history of obesity, and residential history. We used as air pollution indicator modelled NO_X levels at participant home addresses. NO_X levels were relatively low in all studied cities (mean 10.1, SD 5.6) ranging from min 0.5 to max 63.8 µg/m³ (Table S2). People who reported that they can hear more noise in their bedroom also had a higher exposure to NO_X (Fig. S5, Fig. S6). Adjustments for air pollution did not change the positive association of obesity markers with noise hearing markers. The stratified analysis showed that association between noise hearing in bedroom and BMI is statistically significant only for these participants, who had low exposure to NOx (Fig. 1).

The associations between BMI and self-reported traffic noise in bedroom were not statistically significant in subgroups of study centers (Fig. 1). In all study centers, the associations were positive, except for Uppsala, where the association is negative, although not statistically significant. The population restriction for only those who had no family history of obesity, and to those who have lived at the same address for at least 10 years, did not change the results.

Our results confirm the positive association between traffic noise and adiposity that have been previously shown by number of studies (Cai et al., 2020; Christensen et al., 2016, 2015; Foraster et al., 2018; Gui et al., 2022; Huang et al., 2020; Li et al., 2021; Pyko et al., 2017, 2015; van Kempen et al., 2017; Wallas et al., 2019). In the present study we found clear positive relationships between perceived traffic noise in the bedroom and all used obesity markers (WC, BMI, being overweight, obesity, and abdominal obesity), however, only for women. For men, there were no associations, or they were rather opposite. These results are like the findings of Li et al. (2021), who measured nocturnal traffic noise in the bedroom for a week, and found that higher noise exposure increases the BMI among women, but not among men. Furthermore, several other previous studies with different noise exposure metrics (perceived or modelled) have found statistically significant associations between traffic noise and different obesity markers, mainly among women (Foraster et al., 2018; Huang et al., 2020; Oftedal et al., 2015; Pyko et al., 2017, 2015). Further research should be undertaken to confirm the possible important effect modification by sex and to find a pathophysiological explanation for this phenomenon.

The results of our study demonstrate the importance of nocturnal traffic noise in the development of obesity or being overweight. It has been suggested that the intermittent nature of nocturnal road traffic is disturbing to sleep continuity (Sanok et al., 2022). It can lead to sleep disturbance and deprivation, which in turn can lead to obesity (Basner et al., 2014; Koren and Taveras, 2018; Lin et al., 2020; Zaharna and Guilleminault, 2010). Earlier studies in the same RHINE III cohort found that traffic noise was strongly associated with insomnia symptoms (difficulty initiating sleep, difficulty maintaining sleep, and with early morning awakenings) (Janson et al., 2020) and daytime sleepiness and habitual snoring (Gislason et al., 2016). Basner et al. (2014) has shown that nocturnal noise exposure can lead to obesity without perceived sleep disturbance through stress reactions, as a stress response during sleep has been observed starting from very low noise levels and chronic stress has been suggested to contribute to obesity (Bose et al., 2009).

One of the main limitations of the current study is the self-reported noise exposure data, that might have resulted in some reporting biases. For instance, in study of (Birk et al., 2011), the reported road traffic noise annoyance was compared with modelled exposure and only a fair association between them was found (Spearman correlation r_s = 0.37). However, noise modelling is usually done for the street-side façade of the building at 4 m height, not considering the specific location of each respondent in a building (e.g., street vs back-yard side, exact floor etc.)(The European Parliament and The Council of the European Union, 2002). In present study we did not ask if the participant is annoyed/disturbed by traffic noise, but just if he/she can hear it. This way of forming the question may have reduced the reporting biases.

However, the reporting of hearing noise in the bedroom can be affected by the subjective noise sensitivity of participants. Previous studies have shown that for similar exposure levels, noise sensitive individuals tend to report more annoyance than non-sensitive individuals (WHO Regional Office for Europe, 2018). Taking this fact into account, self-reported noise measures could be a better indicator for annoyance among noise-sensitive people than purely modelled levels (Gong et al., 2022). In the current study, there was no evaluation of the participant’s noise sensitivity, but the interpretation of answers to two different questions can give some information about noise sensitivity. In general, betas and ORs were higher for women who reported noise hearing in the bedroom compared to women who reported a bedroom location nearby a street or reported both noise exposure metrics at the same time. This could reflect the possible effect modification of noise sensitivity, which is consistent with the findings of Oftedal et al (2015), who found associations between modeled levels of road traffic noise and obesity markers (BMI and WC) only among noise sensitive women. In this light, the findings of present study – that show an increase of WC by 4.66 cm and BMI by 1.35 kg/m² if there is very much traffic noise in bedroom – are more relevant for noise sensitive women. In the present analysis with a more objective noise metric, window location nearby a street, the increase of WC was lower (2.22 cm). In the analysis where we used a combined noise indicator, the associations were smallest (WC by 0.67 cm and BMI by 0.31 kg/m²). We assume that the combined variable describes most realistic exposure situation in participants bedroom as it reduces over-reporting. Nnevertheless, current findings suggest that noise exposure might be a risk factor for adiposity also among non-sensitive women, but noise sensitivity can be an important effect modifier that increases the effect of road traffic noise.

Most of the studies that have examined the associations between traffic noise and adiposity, have used modeled noise levels at the most exposed façade of the buildings (Christensen et al., 2016, 2015; Oftedal et al., 2015; Pyko et al., 2017, 2015), but as discussed earlier, the exposure assessment created using indoor self-reported data can also provide some advantages over modeled traffic noise exposure assessments. Studies with modeled noise levels have objective data for time-average outdoor traffic noise, but it remains unclear how much all participants can actually hear the noise, as it depends from noise insulation of the building, in which floor the participant lives, the bedroom’s location toward a street, exposure time etc. For example, in Norway facade insulation reduced the proportion of respondents highly annoyed by traffic noise from 43–15% (Amundsen et al., 2013). Self-reported data, although subjective, can give some information about indoor noise levels at each participant’s home and, therefore, confirm the evidence found in studies with modelled noise data.

Participants, who are exposed to higher levels of road traffic noise, are at the same time also exposed to higher levels of air pollution from road traffic as the source of both pollutions is the same. Presently, we used modelled NO_X concentrations as traffic induced air pollution markers at the home addresses of participants. NO_X has been shown to be an effective marker of local traffic pollution (Madsen et al., 2007). NOx is widely used in other studies focusing to traffic noise associations with adiposity (Cai et al., 2020; Foraster et al., 2018; Oftedal et al., 2015; Pyko et al., 2017, 2015). We saw higher than average NO_X concentrations among participants who reported more noise in bedroom or bedroom window towards street with more traffic (Fig. S5 and S6). This association could be the indicator of the respective quality of noise reporting in the present study, as higher modeled NO_X indicates a higher density of road traffic and should be correlated with higher noise levels from road traffic.

Our results suggest that road traffic noise has an effect on obesity/being overweight, independent of air pollution. In line with other studies (Cai et al., 2020; Foraster et al., 2018; Oftedal et al., 2015; Pyko et al., 2017, 2015), adjustments for the air quality indicator NO_X did not change the positive association between noise exposure metrics and obesity markers. To our knowledge, there is no epidemiological evidence that associated the NO_X with obesity in humans, but recently published studies indicate that another air quality indicator, particulate matter, could increase the risk of being overweight (Lin et al., 2022) and also have a confounding role in association between traffic noise and obesity (Cai et al., 2020). In the analyses of this paper we did not have available data on particulate matter, but this could be included in future studies. Another limitation of our study is that we could not adjust for alcohol consumption, diet, income, ethnicity and mental status, which might have contributed to the risk of obesity. However, we were able to adjust for many other potential confounding factors, including physical activity, working status and education (that also indicate socioeconomic status), type of accommodation (detached house, terraced house or an apartment), and hours spent at home – data which are mostly missing in other studies. A strength of the study was its large multicenter randomly selected study population, which allowed us to also run stratified analysis. Nevertheless, as this is a cross-sectional study, a causal relationship cannot be confirmed.

The findings in this study support the hypothesis that higher exposure to nocturnal indoor road traffic noise increases the risk of being overweight or obese, but only among women. For men, there were no clear associations or even the opposite association. Among women all studied obesity markers (WC, BMI, being overweight, obesity, and abdominal obesity) were positively associated with the self-reported noise hearing level in the bedroom and with the bedroom window’s location towards the street. Further research using longitudinal analysis, should be undertaken to confirm our findings and to examine the possible important effect modification by sex.

BMI: body mass index

DAG: Directed Acyclic Graph

dB: Decibels

HPA: hypothalamic–pituitary–adrenocortical

L_Amax: maximum A‐weighted noise level during a particular measurement

NO_X: nitrogen oxides

OR: odds ratio

RHINE: Respiratory Health in Northern Europe

SD: standard deviation

VIF: variance inflation factor

WC: waist circumference

WHO: World Health Organization

Ethics approval

Informed consent was obtained from each participant and the study was approved by regional committees of medical research ethics in Aarhus (ref number 2011/M-20110106), Bergen (ref number 42.91, 149.2000 and 2010/759), Reykjavik (ref number VSNb2011090016/03.15), Tartu (ref number UT REC 60/3–1998, UT REC 209 T-17, UT REC 225/M-24) and Uppsala (ref number 1990/257, 1998/495 and 2010/068). All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Availability of data and materials

The datasets analyzed during the current study are not publicly available because individual privacy could be compromised, but are available from the corresponding author on reasonable request with permission of ethics committee of the respective country

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Funding

The authors declare that no funding was received for conducting this study.

Author Contributions

TV, HO, AP, KT contributed to the study conception and design. Material preparation, data collection and analysis were performed by TV, HKC, TG, MH, CJ, AJ, EL, VS, JNS, LMandKT. The first draft of the manuscript was written by TV, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.”

Acknowledgements

Not applicable.

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Traffic noise in the bedroom in association with being overweight or obese: a cross-sectional study of the Respiratory Health in Northern Europe cohort

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